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Data science apps: beyond notebooks

Jupyter notebooks are transforming the way we look at computing, coding and problem solving. But is this the only “data scientist experience” that this technology can provide?

In this webinar, Natalino will sketch how you could use Jupyter to create interactive and compelling data science web applications and provide new ways of data exploration and analysis. In the background, these apps are still powered by well understood and documented Jupyter notebooks.

They will present an architecture which is composed of four parts: a jupyter server-only gateway, a Scala/Spark Jupyter kernel, a Spark cluster and a angular/bootstrap web application.

8.
8
Jupyter notebook: why?
Language of choice
The Notebook has support for
over 40 programming
languages, including those
popular in Data Science such as
Python, R, Julia and Scala.
Share notebooks
Notebooks can be shared with
others using email, Dropbox,
GitHub and the Jupyter
Notebook Viewer.
Interactive widgets
Code can produce rich output
such as images, videos, LaTeX,
and JavaScript. Interactive
widgets can be used to
manipulate and visualize data in
realtime.
Big data integration
Leverage big data tools, such as
Apache Spark, from Python, R
and Scala. Explore that same
data with pandas, scikit-learn,
ggplot2, dplyr, etc.

12.
12
Jupyter Notebook
● Narratives and Use Cases
Narratives are collaborative, shareable, publishable, and reproducible. We believe that
Narratives help both yourself and other researchers by sharing your use of Jupyter
projects, technical specifics of your deployment, and installation and configuration tips so
that others can learn from your experiences.
From https://jupyter.readthedocs.io/en/latest/use-cases/content-user.html